It is common for data available to a given user of a computing device to be scattered across a broad range of different data stores associated with a variety of different data sources. Data is often distributed amongst various file folders (e.g., folders containing documents, music files, video files, graphics, etc.), system databases configured to store different kinds of data, and customized databases, such as one or more databases customized for an enterprise resource planning (ERP) application. These are just a few of an essentially unlimited list of locations where data is found. To further complicate the scene, some data will be stored locally while other data is often remotely accessible. Under the circumstances, efficiently accessing relevant data when it is needed can be a real challenge.
As the options for data storage increase, so does the potential for data islands. Generally speaking, a data island is an isolated data store, such as data that is primarily accessible through a specialized interface or application (e.g., an ERP application, a fileshare, the World Wide Web, etc.). A data island typically has little if any connection to other information stores.
Generally, but especially when there are data islands, it can be difficult for a user to navigate through different data stores to find a data item that is likely to be relevant to current needs. In many cases, the tools provided to a user to support navigation through data are somewhat primitive. It is not uncommon for a particular search to be limited to a particular data store or source. For example, a search may be limited to Internet data accessible through a web browser, to data stored in a store associated with a word processing application, or to data accessible through an email application. These are just a few examples of common limitations on searches for data.
Regardless of the scope of a given search, a user is commonly forced to sift through search results (e.g., often in a list format) on a trial and error basis in order to determine relevancy of data to current needs. The queries utilized to request the search results are not commonly configured to take contextual considerations into account, such as why the user is searching for certain information or the kind of information most likely to be useful to the user performing the query. Query results often require lots of time or experience to be effectively reviewed. Often times, query refining becomes necessary to efficiently locate relevant results. Sometimes, re-querying using different syntax or search terms is the most efficient option.
Searching multiple data stores can be a relatively tedious process. Some sources require special technical expertise for searching, such as training in an ERP solution in order to run a query against an appropriate ERP entity. In many cases, different sources will have different user interfaces and entry points, such as different web sites or different applications (e.g., word processing, spreadsheet, Internet browser, etc.).
At least because information is generally not stored in a central repository, a user is often relied upon to make good and informed decisions to get to information that satisfies current needs. This can lead to over-dependence on knowledge islands. Generally speaking, a knowledge island is a person, or a limited set of people, within an organization that are familiar with where data related to their area of expertise can be found. For example, a software company having five thousand employees may have twenty designers on staff. Two of those designers may be deeply involved in a project about which the other eighteen designers know very little. If those two designers leave the company, they very well may take with them significant amounts of their expertise in terms of where data relevant to their project can be found. It is a realistic possibility that the designers that take over the project may have to start from scratch in many regards.
One reason that it can be difficult to pick up where a knowledge island left off is that it is relatively difficult to document organization of useful information from different data sources. Often times, different sources of data will have their own organization tools (e.g., “favorites” noted in an Internet browser, special folders for containing copies or links to a user's documents, etc.). Rarely, if ever, are the various organization tools configured to link related data collections. Given the described disconnectedness, maintaining and organizing information in a useful way is difficult.
A related issue that can arise can be referred to as a process island. Take again the example of a software company having five thousand employees and twenty designers. It would not be uncommon for one of the designers or other employees to have little awareness as to what is going on in the business organization outside of his or her direct responsibilities. Unbeknownst to the designer or other employee, dozens of products could be at any of a variety of different stages in any of a variety of different processes, such as a process that begins at design conception and ends at commercial development. Barring direct person-to-person communication or correspondence specifically intended to inform, the left hand essentially does not know what the right hand is doing. It goes without saying that data most likely to be relative from the perspective of a given domain expert could very depending on where one is at in a given process (e.g., data most pertinent to conception may be very different than data most pertinent to commercial implementation).
Another related issue that can arise can be referred to as a corporate memory island. Take once again the software company example. Suppose there is an employee that is a domain expert for a specific set of customers. If this person leaves the company (e.g., resigns, dies, etc.), then the company may encounter a significant informational setback. With the employee goes the knowledge of what has or has not worked in the past, or of why one strategy may be better than another. The information is in the employee's head or, often in the best case scenario, reflected in old documents or a disorganized filing system. Even if the employee has been very helpful and tried to document everything he had been doing, there may or may not be knowledge about where key information is located and what the applicable context was. Even if the location of the documents is known, it can take a tremendous amount of time to reconstruct the business logic and get fully up to speed.
The discussion above is merely provided for general background information and is not intended for use as an aid in determining the scope of the claimed subject matter. Further, it should also be emphasized that the claimed subject matter is not limited to implementations that solve any or all of the disadvantages of any currently known systems noted in this section.